Razzball’s daily fantasy baseball tools – Streamonator, Hittertron, and DFSBot – are available by subscription in 2015. If you play in roto leagues with daily roster changes or Daily Fantasy Sports like DraftKings, these tools will rock your world. Please see our Subscriptions page for details – including how to get a free subscription by opening up a new Daily Fantasy Sports account. 

In my previous ‘hot hitter‘ post, I found that a hitter’s recent performance (measured as last 3 games and last 5 games) had no value in projecting next game performance once you account for the player’s known skill (as measured by Steamer Rest of Season projections) and the relevant gameday matchup info incorporated into our Hittertron/DFSBot (e.g., quality of opposing pitcher, handedness of opposing pitcher, park factors).

This post will cover whether recent performance by a starting pitcher helps improve the next start projections in our Streamonator and the pitching component of DFSBot.

I am not sure when ‘Last 3 Starts’ data became ubiquitous. It has been in the Probable Pitcher grid forever perched under the MLB standings in the newspaper. Why ‘last 3 games’ and not ‘last 4 games’ or ‘last 2 games’? Does the power of three hold sway outside of comedy and cubic measurement?

Here is an overview of my analysis to determine whether a pitcher’s last 3 starts measure a ‘streakiness’ that could improve our existing daily projections:

  • Create a starting pitcher data set for each start in 2014 where the pitcher had 3 previous starts in the prior 19 days (219 unique pitchers, 3,239 instances) .
  • Compile the pitcher’s stats for those 3 starts.
  • Compile the Streamonator projections for those 3 starts
    • Note: Our Streamonator projections use Steamer Rest of Season projections as a base and adjust based on projected opposing lineup + park factors + whether the start is home or away)
  • Determine the correlations between a pitcher’s ‘next start’ stats versus:  1) their Streamonator projections for that game, 2) their  ‘last 3 start’ stats , and 3) the difference between their Streamonator ‘last 3 start’ projections and their actual ‘last 3 start’ stats.
    • Re: #3, this helps separate ‘streakiness’ from ‘skill`.  If Kyle Kendrick throws three straight quality starts with a 1.50 ERA that is more surprising/meaningful than if Felix Hernandez does.
  • Determine if recent game performance provides additional predictive value by running a regression using Streamonator AND the ‘last 3 start’ stats and then comparing the resulting correlation with the Streamonator correlation.
Start #4 Correlations (r) Of Daily Stat Projections For Pitchers With 3 MLB Starts In Previous 19 Games (219 unique pitchers 3,239 instances)
Streamonator Prev 3 Starts Minus Streamonator Proj Previous 3 Starts Streamonator, Prev 3 Starts Minus Proj (regression on both variables) Improvement (+/-)
W 14.2 2.1 2.6 14.3 0.1
IP 23.3 6.9 18.0 25.8 2.5
H/9 14.9 0.3 2.7 14.9 0.0
K/9 36.2 9.5 25.3 36.6 0.5
BB/9 22.0 2.4 12.1 22.1 0.1
ERA (w/ ERA) 14.0 1.9 2.5 14.0 0.0
ERA (w/ FIP) 14.0 0.3 4.4 14.0 0.0
WHIP 17.3 0.5 4.6 17.3 0.0

The ‘Previous 3 Starts’ data has a slight correlation (outside of IP, K/9, and BB/9) with the next start’s results but these correlations were: 1) lower than Streamonator and 2) Aside from IP and K/9, provided virtually no improvement to the Streamonator data when combined via a regression analysis. The correlations are surprisingly weak for the standard ratios (ERA and WHIP). Last 3 start FIP provides a slight improvement in predicting next start ERA than using Last 3 Start ERA but it still provides almost zero percent improvement to the Streamonator results. Breaking out WHIP into H/9 and BB/9, it is clear that BB/9 is more predictive than H/9 (12.1 correlation vs 2.7) but it still adds almost nothing to the Streamonator projection.

I re-ran the ERA correlation for cases where a pitcher’s ‘Last 3 Starts’ FIP was 1+ standard deviations from its Streamonator projections (1,054 instances). This isolated pitchers performing significantly better or worse than expected and might point to things like underlying injuries or improved health. Streamonator had only an 11.4 ERA correlation (worse than the 14 across all instances) but the previous 3 game FIP was only at 2.0. The difference between the Streamonator and Actual Last 3 Game FIP correlated at 0.7 and the combination of Streamonator + Last 3 Games FIP provided the same 11.4 correlation as Streamonator alone.

If I limit the test to only the best 100 Last 3 Start performances (vs projections) and the worst 100 Last 3 Start performances, the results are below. While the top 100 cases where a pitcher outperformed projections in the last 3 starts (aka the ‘hottest’ pitchers) produces a composite ERA 15% lower than the Streamonator projection, the worst 100 produced a composite ERA 10% lower than the projection. The fact both are higher shows that 1) Streamonator projections were a bit high across the board because Steamer projected for a higher run environment in 2014 (we saw the inverse in the hitter study) and 2) even the ‘hottest’ and ‘coldest’ pitchers bounced back to their expected projections.

Actual Streamonator  INDEX
Composite ERA of Top 100 best FIP in Last 3 Starts vs Projections 3.73 4.28 115
Composite ERA Top 100 worst FIP in Last 3 Starts vs Projections 3.77 4.17 110

The projection that benefited most by comparing last 3 start data vs. Streamonator was innings pitched. Admittedly, my estimated IP for pitchers last year was not very dynamic – it just used previous year’s IP average and adjusted slightly up/down depending on the projected rate stats (e.g., the higher the WHIP, the lower the IP). For instance, Justin Verlander’s projected IP in last year’s Streamonator only ranged from 6.4 to 6.7 IP. I will be incorporating these learnings for 2015.

K/9 also saw a slight boost by looking at the last 3 starts. I find this quite interesting. Is it a sign that pitchers exhibit some streakiness with K’s? Are pitchers with recent K success more likely to go for strikeouts than normal? In any case, the impact is very mild. The resulting formula suggests calculating K/9 as roughly 0.5 + 95%*SON K/9 + 11%*(Last 3 Start K/9 – SON K/9). Given the slight improvement in correlation (36.2% to 36.6%), I am not sure this is worth the trouble.

Below is an alternative view at K/9. I grouped pitchers based on how their actual last 3 start K/9 compared to their projected last 3 start K/9. So a pitcher who threw 12 K/9 in their last 3 starts and was projected for 6 K/9 would be at 200%. The results show a positive relationship between a pitcher’s K/9 performance vs expectations in their previous 3 starts and in their next start. After accounting for a baseline underestimation of K-rates (2% underestimation or .12 K/9), the difference in the highest segment is 0.39 K/9 (7.63-7.02-.12)

Actual Last 3 Start K/9 / Projected Last 3 Start K/9 # of Instances Average K/9 in next start Projected K/9 in next start Index Adjusted Index
All 3239 7.47 7.35 102 100
150+% 119 7.63 7.02 109 107
125-149% 460 7.80 7.40 105 104
100-125% 1035 7.64 7.49 102 100
75-100% 1147 7.40 7.36 100 99
50-75% 420 6.83 6.97 98 96
50% or less 58 7.44 7.35 101 100


As we found with hitters, a pitcher’s performance in their Last 3 Starts provides very little to no predictive value that is not already reflected in our Streamonator/DFSBot pitching projections. The two exceptions are innings pitched (which were overly static in 2014 and could benefit from in-season adjustments) and strikeouts (where last 3 start data provided a slight improvement).

As with the hitter analysis, any projection system that ‘learns something new’ on stats like ERA and WHIP from a pitcher’s last 3 starts is either starting from an imperfect projection base, making bad gameday adjustments, and/or is making a false claim.